




import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


__magic_name__  :  Dict					=       datasets.logging.get_logger(__name__)


__magic_name__  :  Optional[Any]					=       """\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"""

__magic_name__  :  Union[str, Any]					=       """\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"""

__magic_name__  :  Dict					=       """\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"""

__magic_name__  :  Dict					=       {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,					_KWARGS_DESCRIPTION			)
class     SCREAMING_SNAKE_CASE__		(datasets.Metric			):
     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       return datasets.MetricInfo(
           description=_DESCRIPTION       ,						citation=_CITATION       ,						homepage='''https://github.com/google-research/bleurt'''       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string'''       ,						id='''sequence'''						),
                   '''references''': datasets.Value('''string'''       ,						id='''sequence'''						),
               }						)       ,						codebase_urls=['''https://github.com/google-research/bleurt''']       ,						reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']       ,						)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		List[str]						):



       """simple docstring"""
       # check that config name specifies a valid BLEURT model
       if self.config_name == "default":
         logger.warning(
             '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
             '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''						)
         lowerCAmelCase__										=      '''bleurt-base-128'''

       if self.config_name.lower() in CHECKPOINT_URLS:
         lowerCAmelCase__										=      self.config_name.lower()

       elif self.config_name.upper() in CHECKPOINT_URLS:
         lowerCAmelCase__										=      self.config_name.upper()

       else:
         raise KeyError(
             F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""						)

       # download the model checkpoint specified by self.config_name and set up the scorer
       lowerCAmelCase__										=      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
       lowerCAmelCase__										=      score.BleurtScorer(os.path.join(__lowerCamelCase       ,						__lowerCamelCase						)						)



     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		str						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scorer.score(references=__lowerCamelCase       ,						candidates=__lowerCamelCase						)
       return {"scores": scores}








"""simple docstring"""





def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			""""""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[chr(i + 65     ) for i in range(26     )]
      # Remove duplicate characters from key
      lowerCAmelCase    		=			remove_duplicates(key.upper()     )
      lowerCAmelCase    		=			len(SCREAMING_SNAKE_CASE     )
      # First fill cipher with key characters
      lowerCAmelCase    		=			{alphabet[i]: char for i, char in enumerate(SCREAMING_SNAKE_CASE     )}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(SCREAMING_SNAKE_CASE     )				,		26     ):
            lowerCAmelCase    		=			alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  lowerCAmelCase    		=			alphabet[i - offset]
            lowerCAmelCase    		=			char
      return cipher_alphabet


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      return "".join(cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		dict[str, str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			{v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ) for ch in message.upper()     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			input("""Enter message to encode or decode: """     ).strip()
      lowerCAmelCase    		=			input("""Enter keyword: """     ).strip()
      lowerCAmelCase    		=			input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
      try:
            lowerCAmelCase    		=			{"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""     )
      lowerCAmelCase    		=			create_cipher_map(SCREAMING_SNAKE_CASE     )
      print(func(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()






